AI Takes Flight in Airline Pricing
As travelers pack their bags for summer getaways, airlines are deploying artificial intelligence (AI) to squeeze every last dollar from their booking systems. Fetcherr, an Israeli startup, is helping carriers navigate the complex dynamic pricing world with its AI-powered platform.
The company’s AI technology uses reinforcement learning models to simulate pricing scenarios and determine the most profitable strategies under market conditions. Fetcherr’s large market model (LMM) and high-frequency pricing system have attracted the attention of major airlines such as Virgin Atlantic, Azul Brazilian Airlines, and Royal Air Maroc.
“By predicting future market trends and optimizing pricing strategies proactively, Fetcherr’s LMM enables high-frequency, informed decision-making,” Fetcherr CEO and Co-founder Roy Cohen told PYMNTS.
“Just as reinforcement learning is a domain of machine learning in which an agent optimizes its behavior through interactions with a dynamic environment, guided by the feedback of rewards or penalties, Fetcherr uses models to continuously improve and optimize airline revenue management strategies, where the reward to be optimized is a revenue-based KPI decided by the airline,” he added.
Industry Challenges
Fetcherr’s emergence comes at a time when the airline industry is grappling with the challenges of post-pandemic recovery. For instance, JetBlue is cutting back and eliminating routes in response to industry headwinds as it tries to regain profitability.
Despite the challenges faced by airlines, corporate travel has rebounded to match the strong growth seen in leisure travel over the past three years, providing a bright spot for the industry as reported by Delta, United and Alaska Airlines in their first-quarter results.
Fetcherr’s AI-powered pricing system offers a potential solution to optimize revenue management in a rapidly changing market. Cohen highlighted the effectiveness of their technology, stating: “Performance improvements typically range in the higher one-digit percentages, with some networks seeing double-digit gains.”
The use of AI in pricing strategies is part of a larger trend across various industries as companies seek to leverage data and machine learning to gain a competitive edge.
Fetcherr’s founders, with backgrounds in deep learning, algorithmic trading, eCommerce and digitization, saw an opportunity to apply these technologies to the airline industry.
“The founders of Fetcherr were motivated by the limitations of traditional revenue management systems that couldn’t keep pace with rapid market changes,” Cohen said.
However, the development of Fetcherr’s AI system was not without challenges. The company had to overcome issues related to legacy infrastructure and data diversity.
“We streamlined integration by creating an onboarding process that layers our AI over existing systems without major disruptions or investments,” Cohen said. “This was complemented by advanced data processing technologies to normalize varied data sets, enhancing AI performance and decision-making accuracy.”
As the summer travel season progresses, industry experts and competitors alike will closely watch the performance of Fetcherr’s AI-powered pricing system. The company’s existing customers have reported significant revenue improvements, with some networks seeing double-digit gains.
“This success stems from our system’s automation and high-frequency capabilities, enhanced by the large market model (LMM) provided to revenue management (RM) teams and operates across their operational pipeline,” Cohen said.
Looking ahead, Fetcherr plans to expand its AI pricing solutions beyond the airline industry.
“Our large market model is designed to be industry-agnostic, capable of anticipating market dynamics and simulating business cases across various sectors,” Cohen said. “While we continue to drive innovation in the airline industry, our goal is to disrupt other traditional industries as well, empowering them through AI.”
Using AI in pricing strategies raises questions about potential ethical concerns and biases.
“Avoidance of ethical concerns and biases is an integral part of the design process and was taken into account from the first days of the architectural design,” Cohen said. “For example, features having a potential of bias projection are excluded from having access to the system from the early stages.”
Many Uses for AI in Air Travel
Other AI applications are gaining traction across the airline industry.
Delta Air Lines has partnered with Exscientia, a British AI-powered drug discovery company, to explore how AI can enhance customer experience and streamline business processes. Alaska Airlines uses AI to improve fuel efficiency and reduce emissions by optimizing flight paths and speeds.
Meanwhile, low-cost carrier EasyJet uses AI algorithms to predict customer demand better and optimize seat pricing.
Beyond customer-facing applications, airlines are harnessing AI to improve operational efficiency. Airbus has developed an AI tool called “Skywise” that helps airlines monitor aircraft health and predict maintenance needs. Boeing is using machine learning to streamline its supply chain and manufacturing processes.
As the travel industry continues to evolve and adapt to new technologies, companies like Fetcherr are positioning themselves at the forefront of this transformation. The success of their AI-powered pricing system during the summer travel season could have far-reaching implications for the future of airline revenue management and the broader application of AI in business strategies.
“Fetcherr’s long-term vision is to broaden the reach of our AI technology throughout the airline industry, enhancing essential operational aspects to boost growth, efficiency and customer satisfaction,” Cohen said. “We aim to create Air Commerce for the broader travel sector with dynamic pricing, advanced analytics and comprehensive offer management.”
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